Systems and methods for generating an extensible digital formulation network model and implementing an intelligent formulation using the formulation network model

ABSTRACT

A method and system for an accelerated design of a virtual product formulation based on an expert-enhanced quantitative formulation network includes sourcing qualitative expert formulation; creating a qualitative formulation network; extracting qualitative network-expansion data based on a category associated with a target product associated with the qualitative formulation network, creating a second set of network components including formulation variable nodes and formulation edge connections; integrating the second set of network components into the qualitative formulation network; transforming the qualitative formulation network integrated with the second set of network components to a quantitative formulation network; designing at least part of a virtual product formulation based on the quantitative formulation network; and generating a target formulation proposal that likely satisfies the target formulation objective based on executing the virtual product formulation as initialized.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.17/978,960, filed 1 Nov. 2022, claims the benefit of U.S. ProvisionalApplication No. 63/253,492, filed 7 Oct. 2021, and U.S. ProvisionalApplication No. 63/297,410, filed 7 Jan. 2022, which are incorporatedherein in their entireties by this reference.

TECHNICAL FIELD

This invention relates generally to the product formulation technologyfield, and more specifically to a new and useful formulation networkmodel in the product formulation technology field.

BACKGROUND

Modern product formulation may be complex and obfuscated, which maycreate several challenges in product formulation and productexperimentation. In particular, institutional knowledge of formulationdata for a given product is often distributed in some known and unknownspaces. Additionally, formulation knowledge of expert formulators maynot be memorialized in a manner that can be easily transferable into newproduct formulations.

Accordingly, the disconnect in sources of institutional formulationknowledge creates formulation data gaps, formulation data anomalies, andformulation data conflicts that reduce efficiencies, accuracies, and thequality of product formulations without a single source of truth forformulation data.

Thus, there is a need in the product formulation technology to createimproved systems and methods for generating an integrated formulationnetwork model that provides a comprehensive reference structure andadvanced formulation tool.

The embodiments of the present application described herein providetechnical solutions that address, at least the need described above.

BRIEF SUMMARY OF THE INVENTION(S)

In one embodiment, a computer-implemented method for an accelerateddesign of a virtual product formulation based on an expert-enhancedquantitative formulation network includes sourcing, via a web-accessibleinterface, qualitative expert formulation data from one or more expertformulators interfacing with a remote formulation service; at the remoteformulation service that is implemented by a network of distributingcomputing systems: creating a qualitative formulation network based onderiving from the qualitative expert formulation data a first set ofnetwork components including a plurality of distinct formulationvariable nodes representing distinct formulation variables, a pluralityof formulation edge connections representing distinct contributoryrelationships between formulation variables, and a plurality offormulation parameter constraints that bound possible values for each ofthe distinct formulation nodes, wherein creating the qualitativeformulation network includes: mapping in an n-dimensional space theplurality of distinct formulation variable nodes in a relation to atarget formulation objective, and setting a distinct one of theplurality of formulation edge connections between distinct pairs of theplurality of distinct formulation variable nodes based on identifying acontributory relationship between two formulation variable nodesdefining each of the distinct pairs; extracting, from a formulationservice-generated corpus of formulation data, qualitativenetwork-expansion data based on a category associated with a targetproduct associated with the qualitative formulation network, wherein thequalitative network-expansion data, when meshed into the qualitativeformulation network, increases one or more of available formulationvariables and available formulation edge connections within thequalitative formulation network; creating, based on the qualitativenetwork-expansion data, a second set of network components including oneor more formulation variable nodes and one or more formulation edgeconnections; integrating the second set of network components into thequalitative formulation network for expanding the qualitativeformulation network; transforming the qualitative formulation networkintegrated with the second set of network components to a quantitativeformulation network by: (i) converting a distinct qualitative valueassociated with each of the plurality of distinct formulation variablenodes of the qualitative formulation network to a distinct quantitativenode value; (ii) converting a distinct qualitative value associated witheach of the plurality of formulation edge connections of the qualitativeformulation network to a distinct quantitative edge value; designing atleast part of a virtual product formulation based on the quantitativeformulation network, wherein designing the virtual product formulationincludes: extracting a subset of the distinct formulation variablesassociated with the plurality of distinct formulation variable nodes ofthe quantitative formulation network based on the distinct quantitativeedge value associated with each of one or more of the formulation edgeconnections that connect to the subset of the distinct formulationvariables, and setting one or more formulation variables of the virtualproduct formulation using the subset of the distinct formulationvariables associated with the plurality of distinct formulation variablenodes of the quantitative formulation network; and generating a targetformulation proposal that likely satisfies the target formulationobjective based on executing the virtual product formulation asinitialized.

In one embodiment, a computer-implemented method includes sourcing, viaa web-accessible interface, qualitative expert formulation data from oneor more expert formulators associated with a subscriber to a remoteformulation service; creating a qualitative formulation network based onderiving from the qualitative expert formulation data a first set ofnetwork components including a plurality of distinct formulationvariable nodes representing distinct formulation variables and aplurality of formulation edge connections representing distinctcontributory relationships between formulation variables, whereincreating the qualitative formulation network includes: mapping theplurality of distinct formulation variable nodes around a targetformulation objective, and setting a distinct one of the plurality offormulation edge connections between distinct pairs of the plurality ofdistinct formulation variable nodes based on identifying a contributoryrelationship between two formulation variable nodes defining each of thedistinct pairs; extracting, from a formulation service-generated corpusof formulation data, qualitative network-expansion data based on acategory associated with a target product associated with thequalitative formulation network; creating, based on the qualitativenetwork-expansion data, a second set of network components including oneor more formulation variable nodes and one or more formulation edgeconnections; integrating the second set of network components into thequalitative formulation network for expanding the qualitativeformulation network; transforming the qualitative formulation networkintegrated with the second set of network components to a quantitativeformulation network by: (i) converting a distinct qualitative valueassociated with each of the plurality of distinct formulation variablenodes of the qualitative formulation network to a distinct quantitativenode value; (ii) converting a distinct qualitative value associated witheach of the plurality of formulation edge connections of the qualitativeformulation network to a distinct quantitative edge value; initializingat least part of a virtual product formulation based on the quantitativeformulation network, wherein initializing the virtual productformulation includes: extracting a subset of the distinct formulationvariables associated with the plurality of distinct formulation variablenodes of the quantitative formulation network based on the distinctquantitative edge value associated with each of one or more of theformulation edge connections that connect to the subset of the distinctformulation variables, and setting one or more formulation variables ofthe virtual product formulation using the subset of the distinctformulation variables associated with the plurality of distinctformulation variable nodes of the quantitative formulation network; andgenerating a target formulation proposal that likely satisfies thetarget formulation objective based on executing the virtual productformulation as initialized.

In one embodiment, computing one or more probability distributions basedon the distinct qualitative value associated with each of the pluralityof distinct formulation variable nodes and of the distinct qualitativevalue associated with each of the plurality of formulation edgeconnections of the qualitative formulation network, wherein convertingthe distinct qualitative value associated with each of the plurality ofdistinct formulation variable nodes and of the distinct qualitativevalue associated with each of the plurality of formulation edgeconnections of the qualitative formulation network is based on thecomputed one or more probability distributions.

In one embodiment, the computed one or more probability distributionsidentify probabilities that a given formulation variable or a givencombination of formulation variables affect the target formulationobjective, wherein the converting includes assigning the distinctquantitative node value to a target formulation variable node of thequantitative formulation network based on identifying a probabilityvalue along a distinct one of the one or more probability distributionsthat aligns with a given distinct qualitative value associated with thetarget formulation variable node.

In one embodiment, transforming the qualitative formulation network tothe quantitative formulation network includes: replacing each distinctqualitative value associated with each of the plurality of distinctformulation variable nodes of the qualitative formulation network withthe distinct quantitative node value associated with each of theplurality of formulation variable nodes; and replacing each distinctqualitative value associated with each of the plurality of formulationedge connections of the qualitative formulation network with thedistinct quantitative edge value associated with each of the pluralityof formulation edge connections.

In one embodiment, extracting the subset of the distinct formulationvariables includes: defining an extraction query based on the categoryassociated with the target product; querying the quantitativeformulation network based on the extraction query; and returning thesubset of the distinct formulation variables based on querying thequantitative formulation network using the extraction query.

In one embodiment, the virtual product formulation is partiallyconfigured with a plurality of subscriber-defined formulation variables;and the initializing the at least part of the virtual productformulation includes augmenting the plurality of subscriber-definedformulation variables with the subset of the distinct formulationvariables extracted from the quantitative formulation network.

In one embodiment, if the virtual product formulation is not configuredwith any subscriber-defined formulation variables, the initializing theat least part of the virtual product formulation includes configuringthe virtual product formulation with service-generated formulationvariables comprising the subset of the distinct formulation variablesextracted from the quantitative formulation network.

In one embodiment, the target formulation proposal includes a proposedcomposition of formulation variables of the plurality of distinctformulation variables and an associated quantitative value for each ofthe formulation variables of the proposed composition.

In one embodiment, the method further includes generating, via a displayof the web-accessible GUI, a graphical representation of thequantitative formulation network that includes displaying arepresentation of each of the plurality of distinct formulation variablenodes and each of the plurality of formulation edge connections of thequantitative formulation network in relation to the target formulationobjective.

In one embodiment, the display comprising the graphical representationof the quantitative formulation network includes one or more graphicalobjects that, when manipulated by the subscriber, reveals from thequantitative formulation network one or more of the plurality ofdistinct formulation variable nodes that contribute to a satisfaction ofthe target formulation object beyond a set or a minimum contributionamount.

In one embodiment, extracting, from the formulation service-generatedcorpus of formulation data, the qualitative network-expansion dataincludes: identifying historical formulation data that is categoricallysimilar to the target product associated with the qualitativeformulation network; and enhancing a probative value of the qualitativeformulation network by integrating the historical formulation data intothe qualitative expert formulation network.

In one embodiment, a computer-program product includescomputer-executable instructions for sourcing, via a web-accessiblegraphical user interface (GUI), qualitative expert formulation data fromone or more expert formulators associated with a subscriber to a remoteformulation service; creating a qualitative formulation network based onderiving from the qualitative expert formulation data a first set ofnetwork components including a plurality of distinct formulationvariable nodes representing distinct formulation variables and aplurality of formulation edge connections representing distinctcontributory relationships between formulation variables, whereincreating the qualitative formulation network includes: mapping theplurality of distinct formulation variable nodes in relation to a targetformulation objective, and setting a distinct one of the plurality offormulation edge connections between distinct pairs of the plurality ofdistinct formulation variable nodes based on identifying a contributoryrelationship between two formulation variable nodes defining each of thedistinct pairs; extracting, from a formulation service-generated corpusof formulation data, qualitative network-expansion data based on acategory associated with a target product associated with thequalitative formulation network; creating, based on the qualitativenetwork-expansion data, a second set of network components including oneor more additional formulation variable nodes and one or more additionalformulation edge connections; integrating the second set of networkcomponents into the qualitative formulation network that expands thequalitative formulation network; transforming the qualitativeformulation network integrated with the second set of network componentsto a quantitative formulation network by: (i) converting a distinctqualitative value associated with each of the plurality of distinctformulation variable nodes of the qualitative formulation network to adistinct quantitative node value; (ii) converting a distinct qualitativevalue associated with each of the plurality of formulation edgeconnections of the qualitative formulation network to a distinctquantitative edge value; initializing at least part of a virtual productformulation based on the quantitative formulation network, whereininitializing the virtual product formulation includes: extracting asubset of the distinct formulation variables associated with theplurality of distinct formulation variable nodes of the quantitativeformulation network based on the distinct quantitative edge valueassociated with each of one or more of the formulation edge connectionsthat connect to the subset of the distinct formulation variables, andsetting one or more formulation variables of the virtual productformulation using the subset of the distinct formulation variablesassociated with the plurality of distinct formulation variable nodes ofthe quantitative formulation network; and generating a targetformulation proposal that likely satisfies the target formulationobjective based on executing the virtual product formulation asinitialized.

In one embodiment, computing one or more probability distributions basedon the distinct qualitative value associated with each of the pluralityof distinct formulation variable nodes and of the distinct qualitativevalue associated with each of the plurality of formulation edgeconnections of the qualitative formulation network, wherein convertingthe distinct qualitative value associated with each of the plurality ofdistinct formulation variable nodes and of the distinct qualitativevalue associated with each of the plurality of formulation edgeconnections of the qualitative formulation network is based on thecomputed one or more probability distributions.

In one embodiment, the computed one or more probability distributionsidentify probabilities that a given formulation variable or a givencombination of formulation variables affect the target formulationobjective, wherein the converting includes assigning the distinctquantitative node value to a target formulation variable node of thequantitative formulation network based on identifying a probabilityvalue along a distinct one of the one or more probability distributionsthat aligns with a given distinct qualitative value associated with thetarget formulation variable node.

In one embodiment, transforming the qualitative formulation network tothe quantitative formulation network includes: replacing each distinctqualitative value associated with each of the plurality of distinctformulation variable nodes of the qualitative formulation network withthe distinct quantitative node value associated with each of theplurality of formulation variable nodes; and replacing each distinctqualitative value associated with each of the plurality of formulationedge connections of the qualitative formulation network with thedistinct quantitative edge value associated with each of the pluralityof formulation edge connections.

BRIEF DESCRIPTION OF THE FIGURES

FIG. 1 illustrates a schematic representation of a system 100 inaccordance with one or more embodiments of the present application;

FIG. 2 illustrates an example method 200 in accordance with one or moreembodiments of the present application;

FIG. 3 illustrates an example schematic of expert formulator dataintegration in accordance with one or more embodiments of the presentapplication; and

FIG. 4 illustrates an example schematic of a quantitative formulationnetwork in accordance with one or more embodiments of the presentapplication.

DESCRIPTION OF THE PREFERRED EMBODIMENTS

The following description of the preferred embodiments of the inventionis not intended to limit the invention to these preferred embodiments,but rather to enable any person skilled in the art to make and use thisinvention.

1. Intelligent Product Formulation System

As shown in FIG. 1 , a system 100 for intelligent product formulationsand/or experimentations include a remote formulation service 110 andexpert formulation graphical user interface 120.

The remote formulation service 110, which may be referred to herein asthe “formulation service”, may have an integrated communicationconnection with a plurality of distinct sources of formulation dataand/or product data of a target product. The remote formulation service110 preferably enables an integration of formulator expertise datatogether with existing product formulation data and a generation of avisualization of the formulator expertise integrated structure, as shownby way of example in FIG. 3 .

In one or more embodiments, the remote formulation service 110 includesa plurality of distinct formulation modules that provide enhancedformulation capabilities for intelligently generation one or moreformulations of a target product. In such embodiments, the remoteformulation service no includes an insight explorer module, a lab benchmodule, and a workspace module. In one embodiment, the insight explorermodule may enable formulators and/or subscribers to the formulationservice to explore, evaluate, and/or manipulate one or more intelligentformulation tools, such as a quantitative formulation network (e.g.,Digital Brain). In one embodiment, the lab bench module may provide oneor more formulation tools including, but not limited to, a formulationsimulation tool and a formulation optimization tool. In one embodiment,the workspace module may provide a virtual formulation workspace forcreating product formulations, executing formulations, and/or storingformulation result data.

The expert formulation graphical user interface 120, which may besometimes referred to herein as the “expert interface”, may be inoperable control communication with the remote formulation service no.In one or more embodiments, the expert interface 120 may include aformulation application programming interface (API) that may beprogrammatically integrated with one or more APIs of the remoteformulation service no and one or more APIs of one or more sources offormulation data and/or product data.

Additionally, or alternatively, the system 100 may include a machinelearning subsystem (not shown) that may be intelligently configured toassist in automatically generating or setting formulation parametersand/or actively implement simulations and/or optimizations (e.g., forformulation experiments, etc.) of one or more formulations.

Additionally, or alternatively, the machine learning subsystem mayimplement one or more ensembles of trained machine learning models. Theone or more ensembles of machine learning models may employ any suitablemachine learning including one or more of: supervised learning (e.g.,using logistic regression, using back propagation neural networks, usingrandom forests, decision trees, etc.), unsupervised learning (e.g.,using an Apriori algorithm, using K-means clustering), semi-supervisedlearning, reinforcement learning (e.g., using a Q-learning algorithm,using temporal difference learning), (generative) adversarial learning,and any other suitable learning style. Each module of the plurality canimplement any one or more of: a machine learning classifier, computervision model, convolutional neural network (e.g., ResNet), visualtransformer model (e.g., ViT), object detection model (e.g., R-CNN,YOLO, etc.), regression algorithm (e.g., ordinary least squares,logistic regression, stepwise regression, multivariate adaptiveregression splines, locally estimated scatterplot smoothing, etc.), aninstance-based method (e.g., k-nearest neighbor, learning vectorquantization, self-organizing map, etc.), a semantic image segmentationmodel, an image instance segmentation model, a panoptic segmentationmodel, a keypoint detection model, a person segmentation model, an imagecaptioning model, a 3D reconstruction model, a regularization method(e.g., ridge regression, least absolute shrinkage and selectionoperator, elastic net, etc.), a decision tree learning method (e.g.,classification and regression tree, iterative dichotomiser 3, C4.5,chi-squared automatic interaction detection, decision stump, randomforest, multivariate adaptive regression splines, gradient boostingmachines, etc.), a Bayesian method (e.g., naïve Bayes, averagedone-dependence estimators, Bayesian belief network, etc.), a kernelmethod (e.g., a support vector machine, a radial basis function, alinear discriminate analysis, etc.), a clustering method (e.g., k-meansclustering, density-based spatial clustering of applications with noise(DBSCAN), expectation maximization, etc.), a bidirectional encoderrepresentation from transformers (BERT) for masked language model tasksand next sentence prediction tasks and the like, variations of BERT(i.e., ULMFiT, XLM UDify, MT-DNN, SpanBERT, RoBERTa, XLNet, ERNIE,KnowBERT, VideoBERT, ERNIE BERT-wwm, MobileBERT, TinyBERT, GPT, GPT-2,GPT-3, GPT-4 (and all subsequent iterations), ELMo, content2Vec, and thelike), an associated rule learning algorithm (e.g., an Apriorialgorithm, an Eclat algorithm, etc.), an artificial neural network model(e.g., a Perceptron method, a back-propagation method, a Hopfieldnetwork method, a self-organizing map method, a learning vectorquantization method, etc.), a deep learning algorithm (e.g., arestricted Boltzmann machine, a deep belief network method, aconvolution network method, a stacked auto-encoder method, etc.), adimensionality reduction method (e.g., principal component analysis,partial lest squares regression, Sammon mapping, multidimensionalscaling, projection pursuit, etc.), an ensemble method (e.g., boosting,bootstrapped aggregation, AdaBoost, stacked generalization, gradientboosting machine method, random forest method, etc.), and any suitableform of machine learning algorithm. Each processing portion of thesystem 100 can additionally or alternatively leverage: a probabilisticmodule, heuristic module, deterministic module, or any other suitablemodule leveraging any other suitable computation method, machinelearning method or combination thereof. However, any suitable machinelearning approach can otherwise be incorporated in the system 100.Further, any suitable model (e.g., machine learning, non-machinelearning, etc.) may be implemented in the various systems and/or methodsdescribed herein.

2. Method for Generating an Extensible Formulation Network Model

As shown by reference to FIG. 2 , a method 200 for generating anextensible quantitative formulation network model includes sourcing aplurality of corpora of formulation data S210, evaluating expertformulation data and creating a qualitative formulation network S220,quantifying expert formulation data S230, and tuning a quantitativeformulation network S240. The method 200 optionally includes iterativelyevolving a quantitative formulation network S250.

2.10 Sourcing Formulation Intelligence Data

S210, which includes sourcing a plurality corpora of formulation data,may function to collect and/or obtain distinct corpora of formulationdata for a target product from one or more distinct sources offormulation data. Preferably, S210 when being implemented by a remoteformulation service may function to interface, via a graphical userinterface (GUI) or application programming interface, with a subscriberto the remote service implementing the method 200 and/or the system 100for identifying and collecting the formulation data for the targetproduct of the subscriber. In a preferred embodiment, at least onecorpus of formulation data may be sourced via collecting historical orpast formulation data derived from one or more formulation attempts (ifany) for a target product.

Existing Formulation Data Upload

In one or more embodiments, S210 may function to source a corpus ofhistorical formulation data from one or more distinct sources ofhistorical formulation data of a subscribing user to one or more datarepositories of the remote formulation service. In such embodiments,S210 may function to operably connect via a network and/orprogrammatically integrate a formulation service or system implementingthe method 200 to the one or more distinct formulation data sources fora target product. One or more points of integration or connection,preferably, enable a discovery of and access to sources of historicalformulation data and may further establish one or more channels throughwhich selective portions of historical formulation data may be uploadedfor evaluation and processing.

In one or more embodiments, the historical formulation data preferablyincludes quantitative data describing one or more formulation findingsor results, formulation criteria and/or experimentation criteria forcreating and/or revising a target product. The historical formulationdata may additionally include results and/or outcomes of variousexperimentations performed for a product formulation discovery.

Additionally, or alternatively, when sourcing historical formulationdata, S210 may function to direct and/or store each distinct type ofhistorical formulation data to a distinct corpus (with the formulationservice). In this way, S210 may function to delineate each distinct typeof historical formulation data for downstream processing including, butnot limited to, one or more component contributions, one or moreexperimental conditions, and/or component relationship processing. Itshall be recognized that, while each distinct type of historicalformulation data may be stored in a distinct corpus, together theplurality of distinct corpora of historical formulation data may definea global corpus of historical formulation data.

Expert Formulation Data Corpus|Expert Initiation

Additionally, or alternatively, S210 may function to source a corpus ofexpert-based formulation data from one or more distinct formulationexperts. In a preferred embodiment, S210 may function to implement oneor more automated formulation data workflows that, when executed,automatically interfaces with one or more formulation experts forsystematically collecting expert formulation data via expertise prompts,expertise queries, data aggregation portals, formulation inquiries,and/or the like. Expert formulation data, as referred to hereinpreferably relates to a collection of human expert know-how and/or humanexpert aptitude in product criteria and product formulation criteriathat is not generally known or available from other sources of productformulation sources. Accordingly, an expert formulator, as referred toherein, preferably relates to a system or human-expert having experiencein formulating a category or type of target product for more than athreshold period of time (i.e., a minimum number of years of formulationexperience or minimum number of formulation experiments performed asdetermined by industry professionals, policy, or guidance).

In one or more embodiments, when sourcing expert formulation data, S210may function to implement a knowledge aggregation interface or portal(e.g., a web-accessible graphical user interface being operablycontrolled by a remote formulation service) that may be accessed orpresented to a target expert for collection expert formulation data. Viathe knowledge aggregation interface, S210 may function to execute one ormore automated formulation data workflows that may operate to collectdata for a plurality of formulation and product criteria or domains(e.g., topics) such as, but not limited to, expected product outcomes(e.g., variables), key product components (e.g., ingredients),formulation space conditions (e.g., lab conditions) that influenceproduct outcomes, relationships between product components, constraintsbetween relationships between product components, key relationshipsbetween product components and formulation space conditions, constraintsbetween relationships between relationships between product componentsand formulation space conditions, synergistic relationships betweenproduct components, synergies between product components and formulationspace conditions. In such embodiments, S210 may additionally oralternatively function to store responses and/or data collected for agiven domain or topic in a distinct corpus.

Additionally, or alternatively, S210 may function to automaticallyperform a mapping between expert response data based on formulationexpertise queries and a potential graphical node or a potentialgraphical edge of a likely qualitative formulation network. In one ormore embodiments, each expertise prompt or expertise query presented viathe web-accessible interface may be digital associated with an entry ofa mapping data structure, such that a response to a distinct expertiseprompt or expertise query may be routed to or input into a specificentry location within the mapping data structure (e.g., reference tableor the like). Accordingly, in such embodiments, S210 may function tocreate a nodes and edges mapping, which may be in the form of areference table or any suitable data structure, that aligns each answerof a formulation expert to a creation of a graphical node or a graphicaledge in a qualitative formulation network. The nodes and edges mappingmay be used as an input in a generation of the qualitative formulationnetwork, as described in more detail herein.

Additionally, or alternatively, in some embodiments, the one or moreautomated formulation data workflows that may be executed for collectingformulation and product criteria data may be informed and/or derivedbased on an identification of a product type or product category forwhich formulation experiments may be desired.

Sourcing External & Miscellaneous Product Intelligence Data

Additionally, or alternatively, S210 may function to source externalformulation and product intelligence data beyond formulation and/orproduct intelligence associated with a subscriber. In one or moreembodiments, external formulation and product intelligence data mayinclude data sourced from and/or via interactions with third parties(e.g., component or ingredient supplier intelligence) that support aproduct commercialization of a target product. Additionally, oralternatively, formulation and product intelligence data may includedata sourced from users of a target product. Accordingly, externalformulation and product intelligence data may be sourced from anyexternal activity, pre-product formulation activity, and post-productformulation activity (e.g., commercialization activities including, butnot limited to, product packaging activities, product processingactivities, product delivery activities, and the associated trial anderror data derived and/or obtained via observations of these activities.

2.20 Feature Correlation Discovery|Formulation Network Creation

S220, which includes evaluating the expert formulation data and creatinga formulation network, may function to derive formulation intelligencedata from the expert formulation data and automatically generate aqualitative formulation network.

Evaluation|Deriving Formulation Intelligence Data

In one or more embodiments, an evaluation of a corpus of expertformulation data includes deriving a corpus of formulation intelligencedata. Formulation intelligence data, as referred to herein, preferablyrelate to a collection of computed inferences that may identify one ormore formulation nexus between two or more formulation criteria and/orbetween one or more formulation criteria and a formulation outcome. Forinstance, an evaluation of the corpus of expert formulation data mayfunction to identify relationships between distinct pairs of productcomponents (e.g., product ingredients, product variables, productconditions, and/or the like), prioritize the distinct pairings based onrelationship strength, and identify or expose the product componentrelationships that rank highest.

In one or more embodiments, an evaluation of the corpus of expertformulation data may include an assessment of a nodes and edges mappingdata structure that may inform a creation of one or more graphical nodesand one or more connector edges within the qualitative formulationnetwork. In one implementation, S220 may function to set or create adistinct graphical node within the qualitative formulation network foreach expert response or entry within the mapping data structure thatidentifies a formulation variable or component, such as an ingredient orformulation condition. In this one implementation, S220 may function toset or create a distinct graphical edge between distinct pairs ofgraphical nodes within the qualitative formulation network for eachexpert response or entry within the mapping data structure thatidentifies a contributory relationship between formulation variables orcomponents to a target formulation outcome or objective. A contributoryrelationship, as referred to herein, preferably relates to a connectionor a relationship between two formulation variables that have a likelyinfluence (i.e., increase or decrease) or effect on a value of aformulation outcome or formulation objective.

Accordingly, an evaluation of the corpus of expert formulation data mayinclude a discovery and surfacing of relationships between formulationfeatures and/or formulation criteria.

In one implementation, S220 may function to individually assess eachformulation criteria and/or product criteria to identify one or moreformulation or product criteria with the highest contributionprobability to one or more formulation objectives or product formulationoutcomes. That is, in this implementation, S220 may function to simulatea contribution of a single formulation criteria or product criteriatowards a formulation objective for a target product. Accordingly, S220may function to prioritize and/or rank each formulation criteria and/orproduct criteria based on their respective contribution probability.Additionally, or alternatively, S220 may function to identify a set offormulation or product criteria having the highest contributionprobability or a contribution probability satisfying a contributionthreshold (e.g., a minimum contribution value) as top drivers or highestprobability contributors toward formulation objectives.

In an additional or alternative implementation, an evaluation of thecorpus of expert formulation data may include identifying informativerelationships and/or synergistic relationships between formulationcriteria and/or product criteria. In one or more embodiments, S220 mayfunction define a plurality of distinct pairwise between formulationcriteria and/or product criteria that may be assessed to identify one ormore pairwise of formulation criteria having an informative relationshipand/or a synergistic relationship toward a formulation outcome for atarget product. In one embodiment, S220 may function to simulate avariation of values of the criteria of a distinct pairwise to determinewhether the distinct criteria of the target pairwise have an influenceor a contribution (e.g., satisfying or exceeding a contributionthreshold) towards a formulation objective for a target product.

Generating a Qualitative Formulation Network

In one or more embodiments, S220 may function to generate a qualitativeformulation network based on expert formulation data and/or formulationintelligence data derived from the expert formulation data. In apreferred embodiment, the qualitative formulation network includes anetwork graph that includes graphical nodes and graphical edges thatconnect distinct pairs of graphical nodes within the network graph. Insuch preferred embodiment, within the network graph, the graphical nodesmay represent product and/or formulation variables and the graphicaledges may represent a relationship between pairs of graphical nodes.

In one or more embodiments, a size of a graphical node within thequalitative formulation network may indicate a relative importance orcontribution of a product or formulation variable towards a formulationoutcome or formulation objective. As a non-limiting example, the greatera size of a graphical node, the increased or greater the importance ofthe product variable or formulation variable in a formulation of aproduct having a desired attribute (i.e., formulation outcome).Similarly, in such embodiments, a length of, thickness of, valueattributed to, or the like a graphical edge between distinct pairs ofgraphical nodes may indicate a contributory relationship strengthbetween each distinct pair of graphical nodes. In one example, theshorter a length of a graphical edge between graphical nodes, thegreater a relationship between the product variables represented by thegraphical nodes. In this example, an extent of the graphical edgesbetween graphical nodes (e.g., formulation and/or product criteria) anda size or an extent of a graphical node may be based on the formulationintelligence data derived based on the expert formulation data.

Prior Beliefs Integration

Additionally, or alternatively, S220 may function to integrate and/oraugment the qualitative formulation network with one or more corpora offormulation prior data or predetermined formulation data. Formulationprior data, as referred to herein, preferably relate to a collection ofproduct-informed formulation learnings derived from a plurality ofhistorical formulations, product experiments, and/or formulation/productexploratory content (e.g., external literature, etc.). Preferably, S210may function to select a set or corpus of formulation priors based onidentifying a set of formulation prior data having a same or similarcategory as a target product of an intended formulation.

In one or more embodiments, S220 may function to identify a formulationcategory, such as a product category, associated with the qualitativeformulation network and extract a subset of formulation data from thecorpus of service-generated formulation data based on the formulationcategory data. That is, in such embodiments, S220 may function toperform a search of the corpus of service-generated formulation datausing a query that includes or that may be informed by the formulationcategory or a semantically similar formulation category. The result ofthe performed search may include a subset formulation data of the corpusof service-generated formulation data, such as formulation variables,formulation relationships, formulation conditions, formulationprocesses, and/or the like, which S220 preferably converts toformulation variable nodes and/or formulation edges for augmenting thequalitative network. In a preferred embodiment, S220 may evaluate theformulation variable nodes and/or formulation edges of theservice-generated formulation data against the formulation variablenodes and formulation edges of the qualitative network and exclude fromthe set of nodes and edges of the service-generated formulation data anynodes and/or edges that are redundant and/or are overlapping withexisting nodes and edges of the qualitative formulation network.Consequently, S220 may function to propose a set of nodes and edgesderived from the service-generated formulation data that arenon-overlapping with the nodes and edges of the qualitative formulationnetwork.

Accordingly, in a first implementation, S220 may augment a first set offormulation variable nodes and formulation edges derived from expertformulation data with a second set of formulation variable nodes andformulation edges derived from predetermined formulation data curated bya formulation service or formulation system (e.g., system 100)implementing the method 200. In this first implementation, S220 mayfunction to integrate the second set of formulation variable nodes andformulation edges of the service-generated formulation data into theexisting qualitative formulation network. As such, an augmentation ofthe second set of formulation variable nodes and the formulation edgesmay enable and/or improve a robustness of the qualitative network byfilling in unrealized or missing formulation components (variables) andformulation edges (relationships) that may inform one or more targetoutcomes of a formulation experiment or the like.

In a second implementation, S220 may function to modify an extent of oneor more of the formulation variable nodes and/or one or more of theformulation edges of the qualitative formulation network based onformulation intelligence or insights extracted from or derived from theservice-generated formulation data. In such embodiments, S220 mayfunction to use the formulation intelligence to modify an extent ofnodes and/or edges of the qualitative formulation network by adjustingthe qualitative values associated with the one or more nodes and/or theone or more edges of the qualitative formulation network. In anon-limiting example, a qualitative value of a formulation variable nodemay be increased from a first qualitative value (e.g., low contribution)to a second qualitative value (e.g., medium contribution) based onformulation intelligence derived from the service-generated formulationdata indicating a that the target formulation variable node has agreater contribution towards a formulation outcome than indicated byexpert formulator data. Similarly, S220 may perform the converse anddecrease a value of a formulation variable node or edge based on theformulation intelligence. Correspondingly, S220 may function to adapt avirtual representation of each formulation variable node and/or eachformulation edge that is adapted based on the formulation intelligencederived from the service-generated formulation data to increase ordecrease a given node's size (i.e., increased contribution or decreasedcontribution) and/or to modify an extent of a formulation edge (e.g.,shorten or lengthen, thicken or thin out, etc.).

2.30 Quantitative Conversion of Qualitative Formulation Network

S230, which includes quantifying expert formulation data, may functionto convert a qualitative formulation network to a quantitativeformulation network, as shown by way of example in FIG. 4 . In one ormore embodiments, data represented within the qualitative formulationnetwork may include data that may not have a cognate numericalrepresentation but rather may include relative representations of expertunderstandings of formulation and/or product criteria. Accordingly, toincrease the utility of a formulation network in computing new and/orenhanced formulation criteria and/or formulations, S230 may function totransform qualitative data within the formulation network to includecorresponding quantitative values.

In one or more embodiments, converting a qualitative formulation networkto a quantitative formulation network includes computing probabilitydistributions based on formulation variable data and outcome dataderived from attributes of the qualitative formulation network. In suchembodiments, S230 may function to compute probability distributions thatidentify probabilities that a given formulation variable or formulationcomponent and/or a given combination of formulation variables orcomponents contribute to or affect a target formulation outcome.Accordingly, S220 may function to compute a probability distribution foreach distinct formulation variable or each distinct formulation variablecombination, represented within the qualitative formulation network,associated with a qualitative contribution towards a recognized outcomewithin the qualitative network. It shall be recognized that aformulation component may include, but is not limited to, an ingredientfor a product or composition of matter, a process or technique ofcreating or formulating a product or composition, and/or any process ortechnique implemented in a development and/or deployment (e.g.,commercialization) of a product or composition.

Additionally, or alternatively, a quantitative conversion of thequalitative formulation network may include quantifying a contributionof each formulation variable or component of the qualitative formulationnetwork to one or more target outcomes. That is, S230 may function toconvert qualitative data indicative of a formulation variable's orformulation component's contribution to a target outcome to a quantifiedvalue. In one or more embodiments, S230 may function to identify a rangeof qualitative data values within a qualitative dataset of a formulationvariable or component. For example, qualitative ratings (by formulationexperts or the like) for a strength of contribution of a formulationvariable to a formulation outcome may include none, weak, moderate, andstrong. In such an example, a qualitative range for the qualitativedataset of a strength of contribution of the formulation variable to atarget formulation outcome may be none-to-strong (e.g., satisfying orexceeding a set contribution-to-outcome (value) threshold).

In response to identifying a qualitative range for a target formulationvariable, S230 may function to compute a frequency for each qualitativebin (e.g., weak, moderate, strong, etc.) of the range. In someembodiments, S220 may function to quantify each qualitative bin of thequalitative range and/or derive statistical metrics and the like foreach bin based on the frequency/count data associated with the targetformulation variable. For instance, S220 may function to compute aprobability of an event, such as a contribution of a formulationvariable to a formulation outcome, based on identifying a quotient ofthe frequency or count of a distinct (e.g., moderate) qualitativeoutcome over a total number of qualitative outcomes for a givenformulation variable.

Accordingly, S230 may function to evaluate and derive quantitativemetric values for contribution-to-outcome for each target formulationvariable of the qualitative formulation network based on the computedformulation variable-to-formulation outcome probability distributions.In one or more embodiments, once quantitative values may be computed foreach qualitative value of the qualitative formulation network, S230 mayfunction to convert the qualitative formulation network to thequantitative formulation network by augmenting or annotating eachassociated graphical node and/or graphical edge with a correspondingquantitative value. In some embodiments, S230 may function to convertthe qualitative formulation network to the quantitative formulationnetwork by replacing each qualitative value of formulation variablecontribution with a corresponding quantitative metric value.

In one or more embodiments, a conversion of the qualitative formulationnetwork to a quantitative formulation network enables a quantifiedidentification of a number of drivers of formulation performance andformulation outcomes including an identification formulationvariables/components and/or product criteria that are top drivers of oneor more product formulation outcomes, an identification of one or morestrength of relationship values between graphically connected pairs ofnodes (i.e., formulation and/or product criteria), and a revelation of aformulation and product knowledge network.

2.40 Formulation Network Tuning|Expert Augmentation

S240, which includes tuning a quantitative formulation network, mayfunction to evaluate the quantitative network graph and intelligentlyadjust and/or tune one or more quantitative values and/or parameters ofthe quantitative formulation network based on the evaluation. In or moreembodiments, tuning the quantitative formulation network mayadditionally include computing and injecting new quantitative valuesinto one or more components of the quantitative formulation network.

Gap Analysis-Based Tuning

In one or more embodiments, S240 may function to perform a gap analysisof the one or more sections or portions of a quantitative formulationnetwork. In such embodiments, the gap analysis may include identifyingone or more regions of the quantitative formulation network that may bemissing a quantitative value for a graphical node (e.g., a formulationand/or product criteria) and/or that may be missing a quantitative valuefor a graphical edge or connector (e.g., a strength of relation, asynergistic value, or the like).

In such embodiments, S240 may function to tune the quantitativeformulation network based on the gap analysis. In one or moreembodiments, tuning the quantitative formulation network includessourcing one or more quantitative value estimates, quantitativeconfidence values, or quantitative probabilities from one or more expertformulators of a target product. In such embodiments, S240 may functionto present, via formulation user interface or portal, the quantitativeformulation network and/or the one or more gaps in formulation networkdata to the one or more expert formulas with one or more gap-specificprompts for collecting the formulation network data required to addressthe gap. Accordingly, S240 may function to tune and/or update thequantitative formulation network based on a response corpus created fromexpert formulator responses to the one or more gap-specific prompts.

Additionally, or alternatively, tuning the quantitative formulationnetwork may include identifying biased formulation network data andeliminating or mitigating the identified bias from the quantitativeformulation network. Additionally, or alternatively, S240 may functionto identify one or more conflicts in formulation network data within thequantitative formulation network and determine one or more resolutionsto the identified conflicts. In such embodiments, S240 may identifyresolutions to conflicts by interfacing with one or more expertformulators for quantitative insights that may support a resolution ofthe conflict in formulation network data.

Additionally, or alternatively, S240 may include a confirmation and/orvalidation stage for either confirming or validating a state of thestructure of the quantitative formulation network. In one or moreembodiments, validating the quantitative formulation network includesobtaining a consensus on the one or more regions of the structure, thegraphical representations, and/or the quantitative values associatedwith the graphical representation of the quantitative formulationnetwork.

2.50 Iteratively Evolving the Quantitative Formulation Network

Optionally, or additionally, S250 includes iteratively evolving aquantitative formulation network model, may function to periodicallycalibrate or re-calibrate one or more graphical nodes and/or graphicaledge connectors of the quantitative formulation network based on newproduct formulation and/or experimentation data.

In one or more embodiments, when new formulation and/or product data maybe created and/or acquired by a formulation service or systemimplementing the method 200, S250 may function to implement an iterativeupdate loop or feedback loop (e.g., executing S220-S240) that infusesthe new formulation data, product data, and/or formulation intelligencederived from the new formulation data into the quantitative formulationnetwork.

In some embodiments, post-formulation activity data including, but notlimited to, subscriber-associated activities and external activity data(e.g., real-world data) may be harvested and utilized to derive newformulation and/or product intelligence data. In such embodiments, S250may function to initialize a feedback loop or iteration mechanism thatingests the new formulation and/or product intelligence data forextending the quantitative formulation network model by adding orremoving (graphical) network nodes and/or edges, by modifying thestrength of connections (graphical edges) between pairs of networknodes, elaborating relationships via graphical annotations, and/orsimilar tailoring of the formulation network model.

Simulation and Optimization

Additionally, or alternatively, the method 200 or related method mayfunction to implement the quantitative formulation network in anysuitable manner for initializing and/or enabling one or more operationsof an automatic formulation and/or a semi-automatic formulation orformulation refresh of a target product. In a preferred embodiment, thequantitative formulation network may interface and/or be integrated witha plurality of distinct formulation modules. In a non-limiting example,the method 200 may function to enable a programmatic interface or thelike between one or more of a formulation simulation module and anoptimization module of a system 100 or the like. In such an example, theformulation simulation module may communicate a simulation objective tothe quantitative formulation network and the formulation network mayfunction to return simulation parameters for implementing or forexecuting a computer-implemented formulation simulations for a targetproduct. Similarly, an optimization module may function to communicateparameters to be optimized for a target formulation and in response, thequantitative formulation network may function to return optimizationparameters or the like for creating an objective function forinitializing an optimization.

In one embodiment, S250 may function to implement the quantitativeformulation network for initializing new product formulation experimentshaving limited or no product formulation parameters (e.g., a cold startformulation). In such embodiments, a reference or a search query to thequantitative formulation network model may function to identify orextract formulation parameters (and associated parameters values),formulation constraints, and/or the like that may be used in one or moreautomated formulation workflows including, but not limited to anadaptive design of a formulation experiment (semi- or fully) automatedworkflows for creating a target new product. In one or more embodiments,the search query submitted to the quantitative formulation network mayinclude a product category or features of the target of the productformulation. In a non-limiting example, S250 may function to construct asearch query (e.g., network search query) based on the product categoryor product feature data and use the search query to identify or discoverformulation variables and/or formulation parameters having a semanticsimilarity to the product category or product features associatedintended cold start formulation. In this way, in some embodiments, atleast part of a target formulation or formulation experiment may beautomatically configured using the formulation variables and/orformulation parameters values (e.g., formulation variable values and thelike). That is, the formulation variables and/or formulation parametervalues of a target formulation may be set by a formulation serviceimplementing the system and/or methods described herein based on thenetwork search query.

Additionally, or alternatively, S250 may function to implement thequantitative formulation network for informing and/or setting a trainingand/or use of one or more machine learning models assistive in aformulation of a target product. In a non-limited example, a referenceand/or a search query to the quantitative formulation network model mayfunction to curate or return a training data corpus for training agenerative adversarial network or the like that may be used to formulateor generate new ingredients or ingredient combinations for a targetproduct.

3. Computer-Implemented Method and Computer Program Product

Embodiments of the system and/or method can include every combinationand permutation of the various system components and the various methodprocesses, wherein one or more instances of the method and/or processesdescribed herein can be performed asynchronously (e.g., sequentially),concurrently (e.g., in parallel), or in any other suitable order byand/or using one or more instances of the systems, elements, and/orentities described herein.

Although omitted for conciseness, the preferred embodiments may includeevery combination and permutation of the implementations of the systemsand methods described herein.

As a person skilled in the art will recognize from the previous detaileddescription and from the figures and claims, modifications and changescan be made to the preferred embodiments of the invention withoutdeparting from the scope of this invention defined in the followingclaims.

We claim:
 1. A computer-implemented method comprising: at a remoteformulation service that is implemented by a network of distributedcomputing systems: creating a qualitative formulation network based onderiving from qualitative expert formulation data a set of networkcomponents including a plurality of distinct formulation variable nodesrepresenting distinct formulation variables, a plurality of formulationedge connections representing distinct contributory relationshipsbetween the distinct formulation variable nodes, wherein creating thequalitative formulation network includes: mapping in an n-dimensionalspace the plurality of distinct formulation variable nodes in a relationto a target formulation objective, and extracting, from a corpus offormulation data, qualitative network-expansion data based on a categoryassociated with a target product associated with the qualitativeformulation network, wherein the qualitative network-expansion data,when meshed into the qualitative formulation network, increases one ormore of available formulation variables and available formulation edgeconnections within the qualitative formulation network; transforming thequalitative formulation network to a quantitative formulation networkby: (i) converting a distinct qualitative value associated with each ofthe plurality of distinct formulation variable nodes of the qualitativeformulation network to a distinct quantitative node value; (ii)converting a distinct qualitative value associated with each of theplurality of formulation edge connections of the qualitative formulationnetwork to a distinct quantitative edge value; designing at least partof a virtual product formulation based on the quantitative formulationnetwork; and generating a target formulation proposal that likelysatisfies the target formulation objective based on executing thevirtual product formulation as designed.
 2. A computer-implementedmethod comprising: creating a qualitative formulation network based onderiving from the qualitative expert formulation data a set of networkcomponents including a plurality of distinct formulation variable nodesrepresenting distinct formulation variables and a plurality offormulation edge connections representing distinct contributoryrelationships between formulation variables, wherein creating thequalitative formulation network includes: mapping the plurality ofdistinct formulation variable nodes around a target formulationobjective, extracting, from a corpus of formulation data, qualitativenetwork-expansion data based on a category associated with a targetproduct associated with the qualitative formulation network; integratinga second set of network components into the qualitative formulationnetwork for expanding the qualitative formulation network based on thequalitative network-expansion data; transforming the qualitativeformulation network integrated with the second set of network componentsto a quantitative formulation network; setting at least part of avirtual product formulation based on the quantitative formulationnetwork; and generating a target formulation proposal that likelysatisfies the target formulation objective based on executing thevirtual product formulation as initialized.
 3. A computer-programproduct embodied in a non-transitory machine-readable storage mediumstoring computer instructions that, when executed by one or moreprocessors, perform operations comprising: creating a qualitativeformulation network based on deriving from the qualitative expertformulation data a set of network components including a plurality ofdistinct formulation variable nodes representing distinct formulationvariables and a plurality of formulation edge connections representingdistinct contributory relationships between formulation variables,wherein creating the qualitative formulation network includes: mappingthe plurality of distinct formulation variable nodes around a targetformulation objective, extracting, from a corpus of formulation data,qualitative network-expansion data based on a category associated with atarget product associated with the qualitative formulation network;integrating a second set of network components into the qualitativeformulation network for expanding the qualitative formulation networkbased on the qualitative network-expansion data; transforming thequalitative formulation network integrated with the second set ofnetwork components to a quantitative formulation network; setting atleast part of a virtual product formulation based on the quantitativeformulation network; and generating a target formulation proposal thatlikely satisfies the target formulation objective based on executing thevirtual product formulation as initialized.